@Michaelzsguo: Lauren Tan @poteto is an engineer at Cursor, previously worked on React Compiler at Meta, and has been a tech lead and engineering manager at Netflix. She has only been with Cursor for five months. The first month was spent familiarizing with the codebase,...

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Summary

Lauren Tan, as an engineer at Cursor, shares her efficient methods in AI coding, emphasizing building verification capabilities and skill testing to manage AI agent teams, achieving high output and automating PR merges.

Lauren Tan @poteto is an engineer at Cursor, previously worked on React Compiler at Meta, and has been a tech lead and engineering manager at Netflix. She has only been with Cursor for five months. The first month was spent familiarizing with the codebase; last month, she merged 1000 PRs. This month, only 12 days in, she has already merged close to 800. This is not AI slop code, but the code of Cursor that you use every day. Many people, including Boris from Claude Code, have mentioned achieving similar efficiency with coding agents. But few are willing to share their complete working methods. Lauren, in this one-hour video, walks through step by step how she reached this point. She believes that the biggest issue with AI coding is not generating code, but verifying code. If the agent cannot run the product itself, operate the interface, read CPU traces and heap snapshots, open simulators, and reproduce issues, then you still have to check the results. You are the verifier of the entire process and the bottleneck that cannot work in parallel. Lauren's approach is to first establish complete verification capabilities for the agent: enabling it to actually operate the product through Chrome DevTools or simulators, then using a feature map to tell it where each function is and how to access it. This way, even if a colleague just sends a screenshot or a vague bug description, the agent can find the corresponding function, reproduce the issue, and verify the fix. Whenever she finds the agent guessing, missing code, or going in the wrong direction, she writes this failure mode as a skill. Then, she tests these skills like testing code: having multiple sub-agents execute tasks separately, with a coordinator setting the rubric, and another model cross-checking the scores, iterating until the results are stable enough. Now, she even allows agents to automatically merge PRs. One morning, she woke up to 20 PRs automatically merged into main; she checked directly on main, and all was fine. This method is not just prompt engineering tricks, but more like engineering management: first design the environment, process, and acceptance mechanisms, then let the team work in parallel. The only difference is that this team now consists of dozens of coding agents. https://x.com/0xCodez/status/2091980766372639135/video/1…
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Lauren Tan (@poteto) is an engineer at Cursor, previously working on the React Compiler at Meta and serving as a tech lead and engineering manager at Netflix.

She joined Cursor only five months ago. In her first month, she familiarized herself with the codebase. Last month, she merged 1,000 PRs. With only 12 days gone this month, she has already merged nearly 800.

This isn’t AI slop code—it’s the very code of Cursor that you use every day.

Many people, including Boris from Claude Code, have mentioned achieving similar efficiency with coding agents. However, few are willing to fully share their work methods. In this one-hour video, Lauren practically walks through her step-by-step approach to reaching this point.

She believes the biggest challenge in AI coding isn’t generating code but verifying it.

If the agent can’t run the product, interact with the interface, read CPU traces and heap snapshots, open a simulator, and reproduce issues on its own, you still have to check the results. You become the verifier of the entire process—and a bottleneck that can’t parallelize work.

Lauren’s solution is to first equip the agent with complete verification capabilities: allowing it to interact with the product via Chrome DevTools or a simulator, and using a feature map to inform it where each function is and how to access it. This way, even if a colleague shares only a screenshot or a vague bug description, the agent can locate the relevant function, reproduce the issue, and verify the fix.

Whenever she notices the agent guessing, misreading code, or going down the wrong path, she documents that failure mode as a skill. She then tests these skills like code: multiple sub-agents perform tasks, a coordinator sets the rubric, and another model cross-checks the scores—iterating until the results are consistently reliable.

Now, she even allows the agent to automatically merge PRs. One morning, she woke up to find 20 PRs automatically merged into main. After checking them directly on main, she found no issues.

This approach isn’t just about prompt engineering—it’s more like engineering management: designing the environment, workflow, and acceptance criteria first, then letting the team work in parallel. The only difference is that this team is now made up of dozens of coding agents.

https://x.com/0xCodez/status/2091980766372639135/video/1…

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